Overview

Dataset statistics

Number of variables42
Number of observations22544
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory7.2 MiB
Average record size in memory336.0 B

Variable types

Numeric26
Categorical16

Alerts

num_outbound_cmds has constant value "0"Constant
Dataset has 3 (< 0.1%) duplicate rowsDuplicates
service has a high cardinality: 64 distinct valuesHigh cardinality
src_bytes is highly overall correlated with dst_bytes and 9 other fieldsHigh correlation
dst_bytes is highly overall correlated with src_bytes and 7 other fieldsHigh correlation
hot is highly overall correlated with num_compromisedHigh correlation
num_compromised is highly overall correlated with hot and 2 other fieldsHigh correlation
num_root is highly overall correlated with su_attempted and 1 other fieldsHigh correlation
count is highly overall correlated with srv_count and 3 other fieldsHigh correlation
srv_count is highly overall correlated with countHigh correlation
serror_rate is highly overall correlated with srv_serror_rate and 2 other fieldsHigh correlation
srv_serror_rate is highly overall correlated with serror_rate and 2 other fieldsHigh correlation
rerror_rate is highly overall correlated with src_bytes and 8 other fieldsHigh correlation
srv_rerror_rate is highly overall correlated with src_bytes and 7 other fieldsHigh correlation
same_srv_rate is highly overall correlated with src_bytes and 12 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_count is highly overall correlated with dst_host_same_src_port_rate and 1 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with src_bytes and 9 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with src_bytes and 6 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with dst_host_count and 1 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with dst_host_count and 1 other fieldsHigh correlation
dst_host_serror_rate is highly overall correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with serror_rate and 2 other fieldsHigh correlation
dst_host_rerror_rate is highly overall correlated with src_bytes and 9 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly overall correlated with src_bytes and 6 other fieldsHigh correlation
protocol_type is highly overall correlated with serviceHigh correlation
service is highly overall correlated with protocol_type and 3 other fieldsHigh correlation
flag is highly overall correlated with logged_in and 1 other fieldsHigh correlation
logged_in is highly overall correlated with count and 6 other fieldsHigh correlation
su_attempted is highly overall correlated with num_compromised and 2 other fieldsHigh correlation
num_access_files is highly overall correlated with num_compromised and 2 other fieldsHigh correlation
is_guest_login is highly overall correlated with serviceHigh correlation
class is highly overall correlated with same_srv_rate and 6 other fieldsHigh correlation
protocol_type is highly imbalanced (50.8%)Imbalance
flag is highly imbalanced (54.4%)Imbalance
land is highly imbalanced (99.6%)Imbalance
wrong_fragment is highly imbalanced (97.1%)Imbalance
urgent is highly imbalanced (99.7%)Imbalance
num_failed_logins is highly imbalanced (93.5%)Imbalance
root_shell is highly imbalanced (97.5%)Imbalance
su_attempted is highly imbalanced (99.8%)Imbalance
num_shells is highly imbalanced (99.5%)Imbalance
num_access_files is highly imbalanced (98.6%)Imbalance
is_host_login is highly imbalanced (99.4%)Imbalance
is_guest_login is highly imbalanced (81.4%)Imbalance
src_bytes is highly skewed (γ1 = 117.4855361)Skewed
dst_bytes is highly skewed (γ1 = 47.50250475)Skewed
hot is highly skewed (γ1 = 63.45658245)Skewed
num_compromised is highly skewed (γ1 = 91.49689495)Skewed
num_root is highly skewed (γ1 = 91.22542536)Skewed
num_file_creations is highly skewed (γ1 = 143.2395924)Skewed
duration has 19018 (84.4%) zerosZeros
src_bytes has 7626 (33.8%) zerosZeros
dst_bytes has 9366 (41.5%) zerosZeros
hot has 21537 (95.5%) zerosZeros
num_compromised has 22175 (98.4%) zerosZeros
num_root has 22496 (99.8%) zerosZeros
num_file_creations has 22502 (99.8%) zerosZeros
serror_rate has 19194 (85.1%) zerosZeros
srv_serror_rate has 19631 (87.1%) zerosZeros
rerror_rate has 16781 (74.4%) zerosZeros
srv_rerror_rate has 16896 (74.9%) zerosZeros
same_srv_rate has 744 (3.3%) zerosZeros
diff_srv_rate has 15847 (70.3%) zerosZeros
srv_diff_host_rate has 17569 (77.9%) zerosZeros
dst_host_same_srv_rate has 1377 (6.1%) zerosZeros
dst_host_diff_srv_rate has 9194 (40.8%) zerosZeros
dst_host_same_src_port_rate has 12656 (56.1%) zerosZeros
dst_host_srv_diff_host_rate has 16330 (72.4%) zerosZeros
dst_host_serror_rate has 17926 (79.5%) zerosZeros
dst_host_srv_serror_rate has 18916 (83.9%) zerosZeros
dst_host_rerror_rate has 13369 (59.3%) zerosZeros
dst_host_srv_rerror_rate has 15293 (67.8%) zerosZeros

Reproduction

Analysis started2023-02-20 09:32:59.344482
Analysis finished2023-02-20 09:36:23.164437
Duration3 minutes and 23.82 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Distinct624
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.85908
Minimum0
Maximum57715
Zeros19018
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:23.481435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile283
Maximum57715
Range57715
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1407.1766
Coefficient of variation (CV)6.4296014
Kurtosis451.37284
Mean218.85908
Median Absolute Deviation (MAD)0
Skewness15.452797
Sum4933959
Variance1980146
MonotonicityNot monotonic
2023-02-20T15:06:23.805435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19018
84.4%
1 587
 
2.6%
4 539
 
2.4%
282 240
 
1.1%
280 216
 
1.0%
5 146
 
0.6%
281 145
 
0.6%
3 120
 
0.5%
2 103
 
0.5%
283 85
 
0.4%
Other values (614) 1345
 
6.0%
ValueCountFrequency (%)
0 19018
84.4%
1 587
 
2.6%
2 103
 
0.5%
3 120
 
0.5%
4 539
 
2.4%
5 146
 
0.6%
6 35
 
0.2%
7 15
 
0.1%
8 15
 
0.1%
9 12
 
0.1%
ValueCountFrequency (%)
57715 1
< 0.1%
54451 1
< 0.1%
53771 1
< 0.1%
47114 1
< 0.1%
42689 1
< 0.1%
38701 1
< 0.1%
22174 1
< 0.1%
20741 1
< 0.1%
16500 1
< 0.1%
13067 1
< 0.1%

protocol_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
tcp
18880 
udp
2621 
icmp
 
1043

Length

Max length4
Median length3
Mean length3.0462651
Min length3

Characters and Unicode

Total characters68675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowtcp
3rd rowtcp
4th rowicmp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 18880
83.7%
udp 2621
 
11.6%
icmp 1043
 
4.6%

Length

2023-02-20T15:06:24.122437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:24.382435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp 18880
83.7%
udp 2621
 
11.6%
icmp 1043
 
4.6%

Most occurring characters

ValueCountFrequency (%)
p 22544
32.8%
c 19923
29.0%
t 18880
27.5%
u 2621
 
3.8%
d 2621
 
3.8%
i 1043
 
1.5%
m 1043
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 22544
32.8%
c 19923
29.0%
t 18880
27.5%
u 2621
 
3.8%
d 2621
 
3.8%
i 1043
 
1.5%
m 1043
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 68675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 22544
32.8%
c 19923
29.0%
t 18880
27.5%
u 2621
 
3.8%
d 2621
 
3.8%
i 1043
 
1.5%
m 1043
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 22544
32.8%
c 19923
29.0%
t 18880
27.5%
u 2621
 
3.8%
d 2621
 
3.8%
i 1043
 
1.5%
m 1043
 
1.5%

service
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct64
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
http
7853 
private
4774 
telnet
1626 
pop_3
1019 
smtp
934 
Other values (59)
6338 

Length

Max length11
Median length10
Mean length5.3288236
Min length3

Characters and Unicode

Total characters120133
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowprivate
2nd rowprivate
3rd rowftp_data
4th roweco_i
5th rowtelnet

Common Values

ValueCountFrequency (%)
http 7853
34.8%
private 4774
21.2%
telnet 1626
 
7.2%
pop_3 1019
 
4.5%
smtp 934
 
4.1%
domain_u 894
 
4.0%
ftp_data 851
 
3.8%
other 838
 
3.7%
ecr_i 752
 
3.3%
ftp 692
 
3.1%
Other values (54) 2311
 
10.3%

Length

2023-02-20T15:06:24.633438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http 7853
34.8%
private 4774
21.2%
telnet 1626
 
7.2%
pop_3 1019
 
4.5%
smtp 934
 
4.1%
domain_u 894
 
4.0%
ftp_data 851
 
3.8%
other 838
 
3.7%
ecr_i 752
 
3.3%
ftp 692
 
3.1%
Other values (54) 2311
 
10.3%

Most occurring characters

ValueCountFrequency (%)
t 28668
23.9%
p 18184
15.1%
e 10620
 
8.8%
h 9040
 
7.5%
a 8079
 
6.7%
i 7606
 
6.3%
r 6866
 
5.7%
v 4817
 
4.0%
_ 4164
 
3.5%
o 3453
 
2.9%
Other values (28) 18636
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 114214
95.1%
Connector Punctuation 4164
 
3.5%
Decimal Number 1656
 
1.4%
Uppercase Letter 99
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 28668
25.1%
p 18184
15.9%
e 10620
 
9.3%
h 9040
 
7.9%
a 8079
 
7.1%
i 7606
 
6.7%
r 6866
 
6.0%
v 4817
 
4.2%
o 3453
 
3.0%
n 3446
 
3.0%
Other values (15) 13435
11.8%
Decimal Number
ValueCountFrequency (%)
3 1100
66.4%
4 378
 
22.8%
9 45
 
2.7%
5 45
 
2.7%
0 45
 
2.7%
1 30
 
1.8%
2 13
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
Z 45
45.5%
X 15
 
15.2%
I 13
 
13.1%
R 13
 
13.1%
C 13
 
13.1%
Connector Punctuation
ValueCountFrequency (%)
_ 4164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114313
95.2%
Common 5820
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 28668
25.1%
p 18184
15.9%
e 10620
 
9.3%
h 9040
 
7.9%
a 8079
 
7.1%
i 7606
 
6.7%
r 6866
 
6.0%
v 4817
 
4.2%
o 3453
 
3.0%
n 3446
 
3.0%
Other values (20) 13534
11.8%
Common
ValueCountFrequency (%)
_ 4164
71.5%
3 1100
 
18.9%
4 378
 
6.5%
9 45
 
0.8%
5 45
 
0.8%
0 45
 
0.8%
1 30
 
0.5%
2 13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 28668
23.9%
p 18184
15.1%
e 10620
 
8.8%
h 9040
 
7.5%
a 8079
 
6.7%
i 7606
 
6.3%
r 6866
 
5.7%
v 4817
 
4.0%
_ 4164
 
3.5%
o 3453
 
2.9%
Other values (28) 18636
15.5%

flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
SF
14875 
REJ
3850 
S0
2013 
RSTO
 
773
RSTR
 
669
Other values (6)
 
364

Length

Max length6
Median length2
Mean length2.299237
Min length2

Characters and Unicode

Total characters51834
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREJ
2nd rowREJ
3rd rowSF
4th rowSF
5th rowRSTO

Common Values

ValueCountFrequency (%)
SF 14875
66.0%
REJ 3850
 
17.1%
S0 2013
 
8.9%
RSTO 773
 
3.4%
RSTR 669
 
3.0%
S3 249
 
1.1%
SH 73
 
0.3%
S1 21
 
0.1%
S2 15
 
0.1%
OTH 4
 
< 0.1%

Length

2023-02-20T15:06:24.933466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 14875
66.0%
rej 3850
 
17.1%
s0 2013
 
8.9%
rsto 773
 
3.4%
rstr 669
 
3.0%
s3 249
 
1.1%
sh 73
 
0.3%
s1 21
 
0.1%
s2 15
 
0.1%
oth 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 18692
36.1%
F 14875
28.7%
R 5963
 
11.5%
E 3850
 
7.4%
J 3850
 
7.4%
0 2015
 
3.9%
T 1448
 
2.8%
O 779
 
1.5%
3 249
 
0.5%
H 77
 
0.1%
Other values (2) 36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49534
95.6%
Decimal Number 2300
 
4.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 18692
37.7%
F 14875
30.0%
R 5963
 
12.0%
E 3850
 
7.8%
J 3850
 
7.8%
T 1448
 
2.9%
O 779
 
1.6%
H 77
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 2015
87.6%
3 249
 
10.8%
1 21
 
0.9%
2 15
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 49534
95.6%
Common 2300
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 18692
37.7%
F 14875
30.0%
R 5963
 
12.0%
E 3850
 
7.8%
J 3850
 
7.8%
T 1448
 
2.9%
O 779
 
1.6%
H 77
 
0.2%
Common
ValueCountFrequency (%)
0 2015
87.6%
3 249
 
10.8%
1 21
 
0.9%
2 15
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 18692
36.1%
F 14875
28.7%
R 5963
 
11.5%
E 3850
 
7.4%
J 3850
 
7.4%
0 2015
 
3.9%
T 1448
 
2.8%
O 779
 
1.5%
3 249
 
0.5%
H 77
 
0.1%
Other values (2) 36
 
0.1%

src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1149
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10395.45
Minimum0
Maximum62825648
Zeros7626
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:25.247436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median54
Q3287
95-th percentile15876
Maximum62825648
Range62825648
Interquartile range (IQR)287

Descriptive statistics

Standard deviation472786.43
Coefficient of variation (CV)45.48013
Kurtosis14714.398
Mean10395.45
Median Absolute Deviation (MAD)54
Skewness117.48554
Sum2.3435503 × 108
Variance2.2352701 × 1011
MonotonicityNot monotonic
2023-02-20T15:06:25.584439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7626
33.8%
1 473
 
2.1%
105 362
 
1.6%
54540 325
 
1.4%
44 313
 
1.4%
1032 311
 
1.4%
2599 293
 
1.3%
32 247
 
1.1%
28 235
 
1.0%
12 229
 
1.0%
Other values (1139) 12130
53.8%
ValueCountFrequency (%)
0 7626
33.8%
1 473
 
2.1%
2 1
 
< 0.1%
5 9
 
< 0.1%
6 17
 
0.1%
7 11
 
< 0.1%
8 53
 
0.2%
9 13
 
0.1%
10 16
 
0.1%
11 8
 
< 0.1%
ValueCountFrequency (%)
62825648 1
< 0.1%
31645608 1
< 0.1%
6291668 1
< 0.1%
3886954 1
< 0.1%
3131464 1
< 0.1%
2194619 1
< 0.1%
1262796 1
< 0.1%
501760 2
< 0.1%
294812 1
< 0.1%
286040 1
< 0.1%

dst_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3650
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2056.0188
Minimum0
Maximum1345927
Zeros9366
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:25.924434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median46
Q3601
95-th percentile8314
Maximum1345927
Range1345927
Interquartile range (IQR)601

Descriptive statistics

Standard deviation21219.298
Coefficient of variation (CV)10.320576
Kurtosis2629.4807
Mean2056.0188
Median Absolute Deviation (MAD)46
Skewness47.502505
Sum46350888
Variance4.5025859 × 108
MonotonicityNot monotonic
2023-02-20T15:06:26.533460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9366
41.5%
44 819
 
3.6%
93 708
 
3.1%
1 362
 
1.6%
174 329
 
1.5%
8314 319
 
1.4%
293 285
 
1.3%
15 206
 
0.9%
597 197
 
0.9%
146 161
 
0.7%
Other values (3640) 9792
43.4%
ValueCountFrequency (%)
0 9366
41.5%
1 362
 
1.6%
3 6
 
< 0.1%
4 10
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
12 11
 
< 0.1%
15 206
 
0.9%
17 4
 
< 0.1%
22 2
 
< 0.1%
ValueCountFrequency (%)
1345927 1
< 0.1%
1288652 1
< 0.1%
1285078 1
< 0.1%
1171108 1
< 0.1%
925244 1
< 0.1%
834163 1
< 0.1%
537280 1
< 0.1%
511712 1
< 0.1%
383476 1
< 0.1%
364200 1
< 0.1%

land
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22537 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22537
> 99.9%
1 7
 
< 0.1%

Length

2023-02-20T15:06:26.836436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:27.067436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22537
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22537
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22537
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22537
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22537
> 99.9%
1 7
 
< 0.1%

wrong_fragment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22444 
1
 
55
3
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22444
99.6%
1 55
 
0.2%
3 45
 
0.2%

Length

2023-02-20T15:06:27.259435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:27.494438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22444
99.6%
1 55
 
0.2%
3 45
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 22444
99.6%
1 55
 
0.2%
3 45
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22444
99.6%
1 55
 
0.2%
3 45
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22444
99.6%
1 55
 
0.2%
3 45
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22444
99.6%
1 55
 
0.2%
3 45
 
0.2%

urgent
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22534 
1
 
5
2
 
4
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22534
> 99.9%
1 5
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%

Length

2023-02-20T15:06:27.692437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:27.941440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22534
> 99.9%
1 5
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22534
> 99.9%
1 5
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22534
> 99.9%
1 5
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22534
> 99.9%
1 5
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22534
> 99.9%
1 5
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%

hot
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1053939
Minimum0
Maximum101
Zeros21537
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:28.155470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum101
Range101
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92842804
Coefficient of variation (CV)8.8091253
Kurtosis6342.2106
Mean0.1053939
Median Absolute Deviation (MAD)0
Skewness63.456582
Sum2376
Variance0.86197862
MonotonicityNot monotonic
2023-02-20T15:06:28.407436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 21537
95.5%
2 846
 
3.8%
1 90
 
0.4%
4 20
 
0.1%
3 12
 
0.1%
5 11
 
< 0.1%
7 9
 
< 0.1%
6 6
 
< 0.1%
18 3
 
< 0.1%
22 2
 
< 0.1%
Other values (6) 8
 
< 0.1%
ValueCountFrequency (%)
0 21537
95.5%
1 90
 
0.4%
2 846
 
3.8%
3 12
 
0.1%
4 20
 
0.1%
5 11
 
< 0.1%
6 6
 
< 0.1%
7 9
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
101 1
 
< 0.1%
30 2
 
< 0.1%
22 2
 
< 0.1%
19 1
 
< 0.1%
18 3
 
< 0.1%
15 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
7 9
< 0.1%
6 6
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22066 
1
 
473
3
 
3
2
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22066
97.9%
1 473
 
2.1%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Length

2023-02-20T15:06:28.653436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:28.904437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22066
97.9%
1 473
 
2.1%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22066
97.9%
1 473
 
2.1%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22066
97.9%
1 473
 
2.1%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22066
97.9%
1 473
 
2.1%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22066
97.9%
1 473
 
2.1%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

logged_in
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
12575 
1
9969 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12575
55.8%
1 9969
44.2%

Length

2023-02-20T15:06:29.119435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:29.349435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12575
55.8%
1 9969
44.2%

Most occurring characters

ValueCountFrequency (%)
0 12575
55.8%
1 9969
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12575
55.8%
1 9969
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12575
55.8%
1 9969
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12575
55.8%
1 9969
44.2%

num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11989886
Minimum0
Maximum796
Zeros22175
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:29.565444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum796
Range796
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2695972
Coefficient of variation (CV)60.631077
Kurtosis8930.6074
Mean0.11989886
Median Absolute Deviation (MAD)0
Skewness91.496895
Sum2703
Variance52.847044
MonotonicityNot monotonic
2023-02-20T15:06:29.829467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 22175
98.4%
1 325
 
1.4%
2 11
 
< 0.1%
4 4
 
< 0.1%
8 4
 
< 0.1%
3 3
 
< 0.1%
5 3
 
< 0.1%
14 2
 
< 0.1%
49 2
 
< 0.1%
6 2
 
< 0.1%
Other values (13) 13
 
0.1%
ValueCountFrequency (%)
0 22175
98.4%
1 325
 
1.4%
2 11
 
< 0.1%
3 3
 
< 0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
796 1
< 0.1%
611 1
< 0.1%
381 1
< 0.1%
165 1
< 0.1%
57 1
< 0.1%
49 2
< 0.1%
36 1
< 0.1%
25 1
< 0.1%
23 1
< 0.1%
15 1
< 0.1%

root_shell
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22489 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22489
99.8%
1 55
 
0.2%

Length

2023-02-20T15:06:30.097463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:30.320437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22489
99.8%
1 55
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 22489
99.8%
1 55
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22489
99.8%
1 55
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22489
99.8%
1 55
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22489
99.8%
1 55
 
0.2%

su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22540 
2
 
2
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22540
> 99.9%
2 2
 
< 0.1%
1 2
 
< 0.1%

Length

2023-02-20T15:06:30.514473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:30.744467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22540
> 99.9%
2 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22540
> 99.9%
2 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22540
> 99.9%
2 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22540
> 99.9%
2 2
 
< 0.1%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22540
> 99.9%
2 2
 
< 0.1%
1 2
 
< 0.1%

num_root
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11466466
Minimum0
Maximum878
Zeros22496
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:30.947461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum878
Range878
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.0416137
Coefficient of variation (CV)70.131582
Kurtosis8908.3227
Mean0.11466466
Median Absolute Deviation (MAD)0
Skewness91.225425
Sum2585
Variance64.667551
MonotonicityNot monotonic
2023-02-20T15:06:31.197435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 22496
99.8%
1 14
 
0.1%
3 7
 
< 0.1%
4 6
 
< 0.1%
2 4
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
173 1
 
< 0.1%
17 1
 
< 0.1%
23 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
ValueCountFrequency (%)
0 22496
99.8%
1 14
 
0.1%
2 4
 
< 0.1%
3 7
 
< 0.1%
4 6
 
< 0.1%
5 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
878 1
< 0.1%
684 1
< 0.1%
401 1
< 0.1%
173 1
< 0.1%
145 1
< 0.1%
51 1
< 0.1%
45 1
< 0.1%
31 1
< 0.1%
26 1
< 0.1%
23 1
< 0.1%

num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008738467
Minimum0
Maximum100
Zeros22502
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:31.443502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.67684167
Coefficient of variation (CV)77.455425
Kurtosis21133.551
Mean0.008738467
Median Absolute Deviation (MAD)0
Skewness143.23959
Sum197
Variance0.45811465
MonotonicityNot monotonic
2023-02-20T15:06:31.686437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22502
99.8%
1 17
 
0.1%
2 9
 
< 0.1%
4 6
 
< 0.1%
3 5
 
< 0.1%
5 2
 
< 0.1%
100 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 22502
99.8%
1 17
 
0.1%
2 9
 
< 0.1%
3 5
 
< 0.1%
4 6
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
100 1
 
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 6
 
< 0.1%
3 5
 
< 0.1%
2 9
 
< 0.1%
1 17
 
0.1%
0 22502
99.8%

num_shells
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22525 
1
 
15
2
 
3
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22525
99.9%
1 15
 
0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%

Length

2023-02-20T15:06:31.931437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:32.183437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22525
99.9%
1 15
 
0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22525
99.9%
1 15
 
0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22525
99.9%
1 15
 
0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22525
99.9%
1 15
 
0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22525
99.9%
1 15
 
0.1%
2 3
 
< 0.1%
5 1
 
< 0.1%

num_access_files
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22472 
1
 
67
2
 
3
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22472
99.7%
1 67
 
0.3%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2023-02-20T15:06:32.397437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:32.654436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22472
99.7%
1 67
 
0.3%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22472
99.7%
1 67
 
0.3%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22472
99.7%
1 67
 
0.3%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22472
99.7%
1 67
 
0.3%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22472
99.7%
1 67
 
0.3%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22544
100.0%

Length

2023-02-20T15:06:32.878509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:33.113438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22544
100.0%

Most occurring characters

ValueCountFrequency (%)
0 22544
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22544
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22544
100.0%

is_host_login
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
22533 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22533
> 99.9%
1 11
 
< 0.1%

Length

2023-02-20T15:06:33.356435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:33.637439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22533
> 99.9%
1 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 22533
> 99.9%
1 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22533
> 99.9%
1 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22533
> 99.9%
1 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22533
> 99.9%
1 11
 
< 0.1%

is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
0
21903 
1
 
641

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22544
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21903
97.2%
1 641
 
2.8%

Length

2023-02-20T15:06:33.876437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:34.164441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21903
97.2%
1 641
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 21903
97.2%
1 641
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22544
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21903
97.2%
1 641
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 22544
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21903
97.2%
1 641
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21903
97.2%
1 641
 
2.8%

count
Real number (ℝ)

Distinct495
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.028345
Minimum0
Maximum511
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:34.445435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median8
Q3123.25
95-th percentile400.85
Maximum511
Range511
Interquartile range (IQR)122.25

Descriptive statistics

Standard deviation128.53925
Coefficient of variation (CV)1.6264955
Kurtosis3.0264487
Mean79.028345
Median Absolute Deviation (MAD)7
Skewness1.9092954
Sum1781615
Variance16522.338
MonotonicityNot monotonic
2023-02-20T15:06:34.803467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5819
25.8%
2 1777
 
7.9%
3 1295
 
5.7%
4 931
 
4.1%
5 616
 
2.7%
6 381
 
1.7%
511 337
 
1.5%
10 337
 
1.5%
8 335
 
1.5%
7 334
 
1.5%
Other values (485) 10382
46.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5819
25.8%
2 1777
 
7.9%
3 1295
 
5.7%
4 931
 
4.1%
5 616
 
2.7%
6 381
 
1.7%
7 334
 
1.5%
8 335
 
1.5%
9 331
 
1.5%
ValueCountFrequency (%)
511 337
1.5%
510 114
 
0.5%
509 94
 
0.4%
508 51
 
0.2%
507 16
 
0.1%
506 14
 
0.1%
505 17
 
0.1%
504 4
 
< 0.1%
503 14
 
0.1%
502 3
 
< 0.1%

srv_count
Real number (ℝ)

Distinct457
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.124379
Minimum0
Maximum511
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:35.143438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median6
Q316
95-th percentile185
Maximum511
Range511
Interquartile range (IQR)15

Descriptive statistics

Standard deviation89.062532
Coefficient of variation (CV)2.8615039
Kurtosis18.924916
Mean31.124379
Median Absolute Deviation (MAD)5
Skewness4.3544292
Sum701668
Variance7932.1346
MonotonicityNot monotonic
2023-02-20T15:06:35.521438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5945
26.4%
2 1911
 
8.5%
3 1400
 
6.2%
4 1048
 
4.6%
5 874
 
3.9%
6 693
 
3.1%
9 626
 
2.8%
7 593
 
2.6%
10 590
 
2.6%
8 579
 
2.6%
Other values (447) 8285
36.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5945
26.4%
2 1911
 
8.5%
3 1400
 
6.2%
4 1048
 
4.6%
5 874
 
3.9%
6 693
 
3.1%
7 593
 
2.6%
8 579
 
2.6%
9 626
 
2.8%
ValueCountFrequency (%)
511 250
1.1%
510 80
 
0.4%
509 64
 
0.3%
508 27
 
0.1%
507 14
 
0.1%
506 12
 
0.1%
505 13
 
0.1%
504 1
 
< 0.1%
503 11
 
< 0.1%
502 2
 
< 0.1%

serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10292362
Minimum0
Maximum1
Zeros19194
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:35.874462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29536686
Coefficient of variation (CV)2.8697676
Kurtosis5.1227744
Mean0.10292362
Median Absolute Deviation (MAD)0
Skewness2.651837
Sum2320.31
Variance0.087241583
MonotonicityNot monotonic
2023-02-20T15:06:36.221466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19194
85.1%
1 2104
 
9.3%
0.03 104
 
0.5%
0.02 94
 
0.4%
0.04 90
 
0.4%
0.01 88
 
0.4%
0.05 86
 
0.4%
0.06 73
 
0.3%
0.07 66
 
0.3%
0.08 64
 
0.3%
Other values (78) 581
 
2.6%
ValueCountFrequency (%)
0 19194
85.1%
0.01 88
 
0.4%
0.02 94
 
0.4%
0.03 104
 
0.5%
0.04 90
 
0.4%
0.05 86
 
0.4%
0.06 73
 
0.3%
0.07 66
 
0.3%
0.08 64
 
0.3%
0.09 40
 
0.2%
ValueCountFrequency (%)
1 2104
9.3%
0.92 1
 
< 0.1%
0.91 1
 
< 0.1%
0.9 2
 
< 0.1%
0.89 3
 
< 0.1%
0.88 4
 
< 0.1%
0.87 7
 
< 0.1%
0.86 5
 
< 0.1%
0.85 8
 
< 0.1%
0.84 5
 
< 0.1%

srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10363511
Minimum0
Maximum1
Zeros19631
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:36.571438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29833161
Coefficient of variation (CV)2.878673
Kurtosis4.9626034
Mean0.10363511
Median Absolute Deviation (MAD)0
Skewness2.6234744
Sum2336.35
Variance0.089001748
MonotonicityNot monotonic
2023-02-20T15:06:37.164438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19631
87.1%
1 2165
 
9.6%
0.03 81
 
0.4%
0.02 60
 
0.3%
0.33 42
 
0.2%
0.17 33
 
0.1%
0.2 32
 
0.1%
0.06 30
 
0.1%
0.25 27
 
0.1%
0.01 26
 
0.1%
Other values (72) 417
 
1.8%
ValueCountFrequency (%)
0 19631
87.1%
0.01 26
 
0.1%
0.02 60
 
0.3%
0.03 81
 
0.4%
0.04 22
 
0.1%
0.05 22
 
0.1%
0.06 30
 
0.1%
0.07 18
 
0.1%
0.08 23
 
0.1%
0.09 16
 
0.1%
ValueCountFrequency (%)
1 2165
9.6%
0.94 2
 
< 0.1%
0.93 1
 
< 0.1%
0.92 1
 
< 0.1%
0.9 2
 
< 0.1%
0.89 1
 
< 0.1%
0.88 4
 
< 0.1%
0.87 6
 
< 0.1%
0.86 3
 
< 0.1%
0.85 3
 
< 0.1%

rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23846301
Minimum0
Maximum1
Zeros16781
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:37.511458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.25
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.41611764
Coefficient of variation (CV)1.7449987
Kurtosis-0.49056924
Mean0.23846301
Median Absolute Deviation (MAD)0
Skewness1.2106484
Sum5375.91
Variance0.17315389
MonotonicityNot monotonic
2023-02-20T15:06:37.873468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16781
74.4%
1 4184
 
18.6%
0.97 114
 
0.5%
0.5 113
 
0.5%
0.98 94
 
0.4%
0.96 90
 
0.4%
0.75 83
 
0.4%
0.95 82
 
0.4%
0.67 71
 
0.3%
0.93 71
 
0.3%
Other values (80) 861
 
3.8%
ValueCountFrequency (%)
0 16781
74.4%
0.08 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 4
 
< 0.1%
0.11 7
 
< 0.1%
0.12 6
 
< 0.1%
0.13 6
 
< 0.1%
0.14 5
 
< 0.1%
0.15 7
 
< 0.1%
0.16 5
 
< 0.1%
ValueCountFrequency (%)
1 4184
18.6%
0.99 51
 
0.2%
0.98 94
 
0.4%
0.97 114
 
0.5%
0.96 90
 
0.4%
0.95 82
 
0.4%
0.94 64
 
0.3%
0.93 71
 
0.3%
0.92 70
 
0.3%
0.91 48
 
0.2%

srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23517876
Minimum0
Maximum1
Zeros16896
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:38.237469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0725
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.0725

Descriptive statistics

Standard deviation0.41621494
Coefficient of variation (CV)1.7697812
Kurtosis-0.41200873
Mean0.23517876
Median Absolute Deviation (MAD)0
Skewness1.2433525
Sum5301.87
Variance0.17323488
MonotonicityNot monotonic
2023-02-20T15:06:38.594439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16896
74.9%
1 4698
 
20.8%
0.5 102
 
0.5%
0.97 76
 
0.3%
0.67 63
 
0.3%
0.98 50
 
0.2%
0.33 50
 
0.2%
0.94 27
 
0.1%
0.4 26
 
0.1%
0.75 24
 
0.1%
Other values (83) 532
 
2.4%
ValueCountFrequency (%)
0 16896
74.9%
0.02 3
 
< 0.1%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.06 2
 
< 0.1%
0.07 4
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 7
 
< 0.1%
0.11 6
 
< 0.1%
ValueCountFrequency (%)
1 4698
20.8%
0.98 50
 
0.2%
0.97 76
 
0.3%
0.96 20
 
0.1%
0.95 15
 
0.1%
0.94 27
 
0.1%
0.93 16
 
0.1%
0.92 12
 
0.1%
0.91 13
 
0.1%
0.9 17
 
0.1%

same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74034466
Minimum0
Maximum1
Zeros744
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:38.957469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.25
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.41249601
Coefficient of variation (CV)0.55716754
Kurtosis-0.90101957
Mean0.74034466
Median Absolute Deviation (MAD)0
Skewness-1.012902
Sum16690.33
Variance0.17015296
MonotonicityNot monotonic
2023-02-20T15:06:39.324438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15860
70.4%
0 744
 
3.3%
0.01 632
 
2.8%
0.02 510
 
2.3%
0.03 487
 
2.2%
0.5 453
 
2.0%
0.07 451
 
2.0%
0.05 446
 
2.0%
0.06 446
 
2.0%
0.04 436
 
1.9%
Other values (65) 2079
 
9.2%
ValueCountFrequency (%)
0 744
3.3%
0.01 632
2.8%
0.02 510
2.3%
0.03 487
2.2%
0.04 436
1.9%
0.05 446
2.0%
0.06 446
2.0%
0.07 451
2.0%
0.08 320
1.4%
0.09 206
 
0.9%
ValueCountFrequency (%)
1 15860
70.4%
0.99 80
 
0.4%
0.98 1
 
< 0.1%
0.95 2
 
< 0.1%
0.94 1
 
< 0.1%
0.93 2
 
< 0.1%
0.92 6
 
< 0.1%
0.91 2
 
< 0.1%
0.9 1
 
< 0.1%
0.89 2
 
< 0.1%

diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.094073811
Minimum0
Maximum1
Zeros15847
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:39.689454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.25913762
Coefficient of variation (CV)2.7546202
Kurtosis7.3134959
Mean0.094073811
Median Absolute Deviation (MAD)0
Skewness2.9967216
Sum2120.8
Variance0.067152307
MonotonicityNot monotonic
2023-02-20T15:06:40.043469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15847
70.3%
0.06 2200
 
9.8%
1 1387
 
6.2%
0.07 1352
 
6.0%
0.08 349
 
1.5%
0.05 247
 
1.1%
0.09 220
 
1.0%
0.1 100
 
0.4%
0.67 97
 
0.4%
0.75 89
 
0.4%
Other values (89) 656
 
2.9%
ValueCountFrequency (%)
0 15847
70.3%
0.01 51
 
0.2%
0.02 38
 
0.2%
0.03 7
 
< 0.1%
0.05 247
 
1.1%
0.06 2200
 
9.8%
0.07 1352
 
6.0%
0.08 349
 
1.5%
0.09 220
 
1.0%
0.1 100
 
0.4%
ValueCountFrequency (%)
1 1387
6.2%
0.99 3
 
< 0.1%
0.98 16
 
0.1%
0.97 24
 
0.1%
0.96 13
 
0.1%
0.95 3
 
< 0.1%
0.94 6
 
< 0.1%
0.93 4
 
< 0.1%
0.92 2
 
< 0.1%
0.91 5
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

Distinct84
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098110362
Minimum0
Maximum1
Zeros17569
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:40.397438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2535453
Coefficient of variation (CV)2.5842867
Kurtosis6.7139241
Mean0.098110362
Median Absolute Deviation (MAD)0
Skewness2.8154988
Sum2211.8
Variance0.064285219
MonotonicityNot monotonic
2023-02-20T15:06:40.759439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17569
77.9%
1 1223
 
5.4%
0.01 245
 
1.1%
0.67 223
 
1.0%
0.5 208
 
0.9%
0.12 198
 
0.9%
0.33 155
 
0.7%
0.1 139
 
0.6%
0.11 135
 
0.6%
0.2 135
 
0.6%
Other values (74) 2314
 
10.3%
ValueCountFrequency (%)
0 17569
77.9%
0.01 245
 
1.1%
0.02 26
 
0.1%
0.03 13
 
0.1%
0.04 33
 
0.1%
0.05 67
 
0.3%
0.06 73
 
0.3%
0.07 108
 
0.5%
0.08 96
 
0.4%
0.09 115
 
0.5%
ValueCountFrequency (%)
1 1223
5.4%
0.96 4
 
< 0.1%
0.95 7
 
< 0.1%
0.94 6
 
< 0.1%
0.93 3
 
< 0.1%
0.92 8
 
< 0.1%
0.91 10
 
< 0.1%
0.9 4
 
< 0.1%
0.89 11
 
< 0.1%
0.88 8
 
< 0.1%

dst_host_count
Real number (ℝ)

Distinct256
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.86941
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:41.106443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1121
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)134

Descriptive statistics

Standard deviation94.035663
Coefficient of variation (CV)0.48504642
Kurtosis-0.55629009
Mean193.86941
Median Absolute Deviation (MAD)0
Skewness-1.09194
Sum4370592
Variance8842.7059
MonotonicityNot monotonic
2023-02-20T15:06:41.463469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 14637
64.9%
1 366
 
1.6%
2 236
 
1.0%
3 200
 
0.9%
4 182
 
0.8%
6 119
 
0.5%
5 115
 
0.5%
8 114
 
0.5%
7 110
 
0.5%
9 98
 
0.4%
Other values (246) 6367
28.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 366
1.6%
2 236
1.0%
3 200
0.9%
4 182
0.8%
5 115
 
0.5%
6 119
 
0.5%
7 110
 
0.5%
8 114
 
0.5%
9 98
 
0.4%
ValueCountFrequency (%)
255 14637
64.9%
254 13
 
0.1%
253 14
 
0.1%
252 15
 
0.1%
251 11
 
< 0.1%
250 10
 
< 0.1%
249 13
 
0.1%
248 16
 
0.1%
247 12
 
0.1%
246 7
 
< 0.1%

dst_host_srv_count
Real number (ℝ)

Distinct256
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.75053
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:41.814469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median168
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)240

Descriptive statistics

Standard deviation111.78397
Coefficient of variation (CV)0.79419929
Kurtosis-1.8336051
Mean140.75053
Median Absolute Deviation (MAD)87
Skewness-0.13405817
Sum3173080
Variance12495.656
MonotonicityNot monotonic
2023-02-20T15:06:42.168438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 7528
33.4%
1 1595
 
7.1%
254 607
 
2.7%
253 398
 
1.8%
2 395
 
1.8%
4 348
 
1.5%
3 341
 
1.5%
18 330
 
1.5%
5 312
 
1.4%
6 308
 
1.4%
Other values (246) 10382
46.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1595
7.1%
2 395
 
1.8%
3 341
 
1.5%
4 348
 
1.5%
5 312
 
1.4%
6 308
 
1.4%
7 272
 
1.2%
8 308
 
1.4%
9 303
 
1.3%
ValueCountFrequency (%)
255 7528
33.4%
254 607
 
2.7%
253 398
 
1.8%
252 110
 
0.5%
251 157
 
0.7%
250 69
 
0.3%
249 91
 
0.4%
248 89
 
0.4%
247 98
 
0.4%
246 72
 
0.3%

dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60872161
Minimum0
Maximum1
Zeros1377
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:42.531467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.07
median0.92
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.43568815
Coefficient of variation (CV)0.71574287
Kurtosis-1.7138115
Mean0.60872161
Median Absolute Deviation (MAD)0.08
Skewness-0.38275992
Sum13723.02
Variance0.18982417
MonotonicityNot monotonic
2023-02-20T15:06:42.882471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9800
43.5%
0 1377
 
6.1%
0.02 869
 
3.9%
0.07 806
 
3.6%
0.05 754
 
3.3%
0.04 740
 
3.3%
0.01 712
 
3.2%
0.99 536
 
2.4%
0.03 522
 
2.3%
0.06 476
 
2.1%
Other values (91) 5952
26.4%
ValueCountFrequency (%)
0 1377
6.1%
0.01 712
3.2%
0.02 869
3.9%
0.03 522
 
2.3%
0.04 740
3.3%
0.05 754
3.3%
0.06 476
 
2.1%
0.07 806
3.6%
0.08 310
 
1.4%
0.09 75
 
0.3%
ValueCountFrequency (%)
1 9800
43.5%
0.99 536
 
2.4%
0.98 212
 
0.9%
0.97 141
 
0.6%
0.96 245
 
1.1%
0.95 148
 
0.7%
0.94 76
 
0.3%
0.93 92
 
0.4%
0.92 84
 
0.4%
0.91 118
 
0.5%

dst_host_diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.090539833
Minimum0
Maximum1
Zeros9194
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:43.243439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.06
95-th percentile0.75
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.22071685
Coefficient of variation (CV)2.4377872
Kurtosis9.4822165
Mean0.090539833
Median Absolute Deviation (MAD)0.01
Skewness3.2556382
Sum2041.13
Variance0.048715926
MonotonicityNot monotonic
2023-02-20T15:06:43.602442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9194
40.8%
0.01 2085
 
9.2%
0.02 1860
 
8.3%
0.06 1839
 
8.2%
0.07 1509
 
6.7%
0.03 1108
 
4.9%
0.05 765
 
3.4%
1 556
 
2.5%
0.04 484
 
2.1%
0.08 475
 
2.1%
Other values (91) 2669
 
11.8%
ValueCountFrequency (%)
0 9194
40.8%
0.01 2085
 
9.2%
0.02 1860
 
8.3%
0.03 1108
 
4.9%
0.04 484
 
2.1%
0.05 765
 
3.4%
0.06 1839
 
8.2%
0.07 1509
 
6.7%
0.08 475
 
2.1%
0.09 357
 
1.6%
ValueCountFrequency (%)
1 556
2.5%
0.99 39
 
0.2%
0.98 33
 
0.1%
0.97 15
 
0.1%
0.96 19
 
0.1%
0.95 32
 
0.1%
0.94 39
 
0.2%
0.93 52
 
0.2%
0.92 32
 
0.1%
0.91 26
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13226091
Minimum0
Maximum1
Zeros12656
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:43.961439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.03
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.30626827
Coefficient of variation (CV)2.315637
Kurtosis3.3566675
Mean0.13226091
Median Absolute Deviation (MAD)0
Skewness2.2562316
Sum2981.69
Variance0.093800251
MonotonicityNot monotonic
2023-02-20T15:06:44.329439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12656
56.1%
0.01 3234
 
14.3%
1 1708
 
7.6%
0.02 968
 
4.3%
0.03 521
 
2.3%
0.04 324
 
1.4%
0.05 248
 
1.1%
0.06 185
 
0.8%
0.08 153
 
0.7%
0.07 149
 
0.7%
Other values (91) 2398
 
10.6%
ValueCountFrequency (%)
0 12656
56.1%
0.01 3234
 
14.3%
0.02 968
 
4.3%
0.03 521
 
2.3%
0.04 324
 
1.4%
0.05 248
 
1.1%
0.06 185
 
0.8%
0.07 149
 
0.7%
0.08 153
 
0.7%
0.09 93
 
0.4%
ValueCountFrequency (%)
1 1708
7.6%
0.99 107
 
0.5%
0.98 29
 
0.1%
0.97 30
 
0.1%
0.96 31
 
0.1%
0.95 33
 
0.1%
0.94 37
 
0.2%
0.93 42
 
0.2%
0.92 23
 
0.1%
0.91 26
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019638485
Minimum0
Maximum1
Zeros16330
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:44.679471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.06
Maximum1
Range1
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.085393883
Coefficient of variation (CV)4.3482929
Kurtosis88.744338
Mean0.019638485
Median Absolute Deviation (MAD)0
Skewness8.775721
Sum442.73
Variance0.0072921152
MonotonicityNot monotonic
2023-02-20T15:06:45.044440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16330
72.4%
0.02 1487
 
6.6%
0.01 1117
 
5.0%
0.03 986
 
4.4%
0.04 904
 
4.0%
0.05 452
 
2.0%
0.06 256
 
1.1%
0.07 153
 
0.7%
1 108
 
0.5%
0.25 68
 
0.3%
Other values (48) 683
 
3.0%
ValueCountFrequency (%)
0 16330
72.4%
0.01 1117
 
5.0%
0.02 1487
 
6.6%
0.03 986
 
4.4%
0.04 904
 
4.0%
0.05 452
 
2.0%
0.06 256
 
1.1%
0.07 153
 
0.7%
0.08 55
 
0.2%
0.09 32
 
0.1%
ValueCountFrequency (%)
1 108
0.5%
0.8 1
 
< 0.1%
0.75 2
 
< 0.1%
0.67 8
 
< 0.1%
0.62 1
 
< 0.1%
0.6 5
 
< 0.1%
0.57 2
 
< 0.1%
0.56 3
 
< 0.1%
0.55 2
 
< 0.1%
0.54 2
 
< 0.1%

dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.097813609
Minimum0
Maximum1
Zeros17926
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:45.406438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27313874
Coefficient of variation (CV)2.7924411
Kurtosis6.0321702
Mean0.097813609
Median Absolute Deviation (MAD)0
Skewness2.7674769
Sum2205.11
Variance0.07460477
MonotonicityNot monotonic
2023-02-20T15:06:45.773442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17926
79.5%
1 1530
 
6.8%
0.01 660
 
2.9%
0.02 314
 
1.4%
0.05 142
 
0.6%
0.33 134
 
0.6%
0.04 130
 
0.6%
0.03 113
 
0.5%
0.07 90
 
0.4%
0.5 90
 
0.4%
Other values (89) 1415
 
6.3%
ValueCountFrequency (%)
0 17926
79.5%
0.01 660
 
2.9%
0.02 314
 
1.4%
0.03 113
 
0.5%
0.04 130
 
0.6%
0.05 142
 
0.6%
0.06 55
 
0.2%
0.07 90
 
0.4%
0.08 29
 
0.1%
0.09 42
 
0.2%
ValueCountFrequency (%)
1 1530
6.8%
0.99 28
 
0.1%
0.98 20
 
0.1%
0.97 32
 
0.1%
0.96 15
 
0.1%
0.95 3
 
< 0.1%
0.94 1
 
< 0.1%
0.92 1
 
< 0.1%
0.91 6
 
< 0.1%
0.89 5
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.099426011
Minimum0
Maximum1
Zeros18916
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:46.143440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.28186628
Coefficient of variation (CV)2.834935
Kurtosis5.4716814
Mean0.099426011
Median Absolute Deviation (MAD)0
Skewness2.6814941
Sum2241.46
Variance0.079448599
MonotonicityNot monotonic
2023-02-20T15:06:46.752440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18916
83.9%
1 1694
 
7.5%
0.01 497
 
2.2%
0.02 214
 
0.9%
0.03 48
 
0.2%
0.06 31
 
0.1%
0.05 28
 
0.1%
0.34 28
 
0.1%
0.55 24
 
0.1%
0.07 23
 
0.1%
Other values (91) 1041
 
4.6%
ValueCountFrequency (%)
0 18916
83.9%
0.01 497
 
2.2%
0.02 214
 
0.9%
0.03 48
 
0.2%
0.04 23
 
0.1%
0.05 28
 
0.1%
0.06 31
 
0.1%
0.07 23
 
0.1%
0.08 16
 
0.1%
0.09 9
 
< 0.1%
ValueCountFrequency (%)
1 1694
7.5%
0.99 2
 
< 0.1%
0.98 17
 
0.1%
0.97 6
 
< 0.1%
0.96 6
 
< 0.1%
0.95 3
 
< 0.1%
0.94 4
 
< 0.1%
0.93 3
 
< 0.1%
0.92 4
 
< 0.1%
0.91 2
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23338494
Minimum0
Maximum1
Zeros13369
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:47.103438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.36
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.3872288
Coefficient of variation (CV)1.6591851
Kurtosis-0.25752505
Mean0.23338494
Median Absolute Deviation (MAD)0
Skewness1.2562211
Sum5261.43
Variance0.14994615
MonotonicityNot monotonic
2023-02-20T15:06:47.471439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13369
59.3%
1 2915
 
12.9%
0.01 695
 
3.1%
0.02 560
 
2.5%
0.04 270
 
1.2%
0.11 197
 
0.9%
0.07 195
 
0.9%
0.05 192
 
0.9%
0.06 186
 
0.8%
0.03 172
 
0.8%
Other values (91) 3793
 
16.8%
ValueCountFrequency (%)
0 13369
59.3%
0.01 695
 
3.1%
0.02 560
 
2.5%
0.03 172
 
0.8%
0.04 270
 
1.2%
0.05 192
 
0.9%
0.06 186
 
0.8%
0.07 195
 
0.9%
0.08 73
 
0.3%
0.09 75
 
0.3%
ValueCountFrequency (%)
1 2915
12.9%
0.99 111
 
0.5%
0.98 53
 
0.2%
0.97 43
 
0.2%
0.96 105
 
0.5%
0.95 110
 
0.5%
0.94 49
 
0.2%
0.93 102
 
0.5%
0.92 29
 
0.1%
0.91 59
 
0.3%

dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22668293
Minimum0
Maximum1
Zeros15293
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size176.2 KiB
2023-02-20T15:06:47.831083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.17
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.40087488
Coefficient of variation (CV)1.7684387
Kurtosis-0.17835669
Mean0.22668293
Median Absolute Deviation (MAD)0
Skewness1.3144105
Sum5110.34
Variance0.16070067
MonotonicityNot monotonic
2023-02-20T15:06:48.186221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15293
67.8%
1 4203
 
18.6%
0.01 444
 
2.0%
0.02 275
 
1.2%
0.03 229
 
1.0%
0.05 127
 
0.6%
0.06 110
 
0.5%
0.07 107
 
0.5%
0.04 95
 
0.4%
0.98 89
 
0.4%
Other values (90) 1572
 
7.0%
ValueCountFrequency (%)
0 15293
67.8%
0.01 444
 
2.0%
0.02 275
 
1.2%
0.03 229
 
1.0%
0.04 95
 
0.4%
0.05 127
 
0.6%
0.06 110
 
0.5%
0.07 107
 
0.5%
0.08 44
 
0.2%
0.09 26
 
0.1%
ValueCountFrequency (%)
1 4203
18.6%
0.98 89
 
0.4%
0.97 55
 
0.2%
0.96 13
 
0.1%
0.95 13
 
0.1%
0.94 13
 
0.1%
0.93 8
 
< 0.1%
0.92 5
 
< 0.1%
0.91 6
 
< 0.1%
0.9 9
 
< 0.1%

class
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size176.2 KiB
anomaly
12833 
normal
9711 

Length

Max length7
Median length7
Mean length6.5692424
Min length6

Characters and Unicode

Total characters148097
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowanomaly
2nd rowanomaly
3rd rownormal
4th rowanomaly
5th rowanomaly

Common Values

ValueCountFrequency (%)
anomaly 12833
56.9%
normal 9711
43.1%

Length

2023-02-20T15:06:48.502205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-20T15:06:48.751189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
anomaly 12833
56.9%
normal 9711
43.1%

Most occurring characters

ValueCountFrequency (%)
a 35377
23.9%
n 22544
15.2%
o 22544
15.2%
m 22544
15.2%
l 22544
15.2%
y 12833
 
8.7%
r 9711
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 148097
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35377
23.9%
n 22544
15.2%
o 22544
15.2%
m 22544
15.2%
l 22544
15.2%
y 12833
 
8.7%
r 9711
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 148097
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35377
23.9%
n 22544
15.2%
o 22544
15.2%
m 22544
15.2%
l 22544
15.2%
y 12833
 
8.7%
r 9711
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35377
23.9%
n 22544
15.2%
o 22544
15.2%
m 22544
15.2%
l 22544
15.2%
y 12833
 
8.7%
r 9711
 
6.6%

Interactions

2023-02-20T15:06:12.492485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:11.752261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:18.744563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:25.250562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:32.109256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:38.557231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:45.308198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:51.869195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:58.214607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:05.842427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:12.836898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:20.041495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:27.248871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:34.213871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:41.442059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:49.580681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:56.913471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:04.525769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:13.001786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:20.391847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:27.603571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:34.851990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:41.976598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:49.701598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:57.027988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:04.683453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:12.782455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:12.030227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:18.995562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:25.505564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:32.356230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:38.819260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:45.553199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:52.120203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:58.468964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:06.115455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:13.342655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:20.313471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:27.522867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:34.483863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:41.718091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:49.879648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:57.186472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:04.844771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:20.711847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:27.908600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:35.127997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:42.249626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:49.982602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:57.322019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:04.967453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:13.081455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:03:25.987562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:32.600226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:39.088258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:45.799199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:52.369200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:58.716964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:06.384458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:13.612657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:20.586472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:27.794868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:34.753840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:41.990090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:50.172649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:57.457442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:05.180769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:13.546762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:50.254632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:57.609018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:05.249454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:13.441479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:12.548228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:19.522563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:03:39.364454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:04:13.893690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:20.868504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:28.082869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:35.039868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:05.879776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:50.538439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:57.931017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:05.545453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:13.720453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:03:39.620423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:04:06.934457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:14.150657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:21.131479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:28.344907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:35.311864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:42.670064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:50.746648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:58.011466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:14.099762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:04:28.636871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:04:43.041065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:04:58.301468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:14.389793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:21.845880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:29.056600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:36.288377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:43.370598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:51.083592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:58.766025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:06.152484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:14.283742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:13.290228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:20.282563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:27.031564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:33.567227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:40.145456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:46.814199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:53.369200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:59.746964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:07.479456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:14.689657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-20T15:05:40.570623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:48.294636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:55.533590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:03.276987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:10.766462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:18.897435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:17.732227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:24.261593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:31.085226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:37.584229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:44.250193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:50.885201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:57.243637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:04.704230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:11.766935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:18.976499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:26.182892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:33.143873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:40.374907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:48.500094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:55.838947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:03.219771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:11.909191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:19.036361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:26.510595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:33.773018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:40.858632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:48.586600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:55.870561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:03.565013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:11.049458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:19.179438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:17.974661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:24.505562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:31.331227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:37.840228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:44.509195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:51.152173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:57.483636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:04.991202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:12.033897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:19.239470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:26.451920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:33.407843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:40.642879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:48.768090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:56.105513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:03.573773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:12.194192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:19.527846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:26.787570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:34.034991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:41.136599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:48.894635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:56.144562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:03.839988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:11.380462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:19.464433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:18.239656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:24.754599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:31.595227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:38.081228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:44.768193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:51.396205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:57.727646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:05.296457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:12.306893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:19.505495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:26.716895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:33.682869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:40.909879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:49.034649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:56.378487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:03.905793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:12.452764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:19.812845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:27.052599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:34.305036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:41.408632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:49.167600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:56.419561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:04.121987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:11.679453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:19.745437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:18.494659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:24.998562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:31.845265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:38.316229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:45.035169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:51.632201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:03:57.967642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:05.572458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:12.574893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:19.771500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:26.981922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:33.949838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:41.175090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:49.302649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:04:56.646438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:04.208776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:12.732764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:20.098877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:27.331571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:34.577990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:41.687629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:49.435600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:05:56.690017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:04.400988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-20T15:06:12.211454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-20T15:06:49.136193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
durationsrc_bytesdst_byteshotnum_compromisednum_rootnum_file_creationscountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateprotocol_typeserviceflaglandwrong_fragmenturgentnum_failed_loginslogged_inroot_shellsu_attemptednum_shellsnum_access_filesis_host_loginis_guest_loginclass
duration1.0000.1540.0370.300-0.0010.0990.095-0.379-0.306-0.089-0.052-0.025-0.0360.211-0.178-0.0870.024-0.074-0.0680.143-0.130-0.0810.0650.0870.1590.0030.0430.2120.1430.0000.0000.0000.0000.1310.1190.4470.0000.2250.0000.0180.126
src_bytes0.1541.0000.6050.1820.2010.0530.044-0.2800.148-0.391-0.342-0.500-0.5040.677-0.6570.167-0.3100.6220.662-0.5970.3080.289-0.387-0.338-0.504-0.5310.0000.2050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
dst_bytes0.0370.6051.0000.2380.2070.0640.055-0.406-0.024-0.377-0.321-0.528-0.5290.588-0.5600.278-0.3350.6360.614-0.5740.0900.348-0.320-0.276-0.475-0.4890.0000.1340.0940.0000.0000.0000.0000.0160.1410.3530.0000.1810.0000.0000.018
hot0.3000.1820.2381.0000.5700.1160.144-0.201-0.165-0.084-0.077-0.089-0.0890.130-0.126-0.0260.034-0.042-0.0260.051-0.089-0.108-0.007-0.0500.048-0.0210.0000.0390.0000.0000.0000.0000.1360.0220.0530.0000.0000.2350.0000.0990.018
num_compromised-0.0010.2010.2070.5701.0000.2320.175-0.063-0.006-0.048-0.044-0.042-0.0410.082-0.0820.0620.0700.0600.058-0.045-0.092-0.071-0.025-0.0080.0590.0880.0000.0000.0000.0000.0000.2580.0000.0070.2690.7070.0000.5800.0000.0000.006
num_root0.0990.0530.0640.1160.2321.0000.489-0.060-0.058-0.019-0.018-0.027-0.0270.029-0.029-0.019-0.018-0.048-0.0340.0210.003-0.007-0.010-0.005-0.014-0.0130.0000.0000.0000.0000.0000.4470.0000.0100.3010.7070.0000.5430.0000.0000.008
num_file_creations0.0950.0440.0550.1440.1750.4891.000-0.055-0.053-0.018-0.017-0.025-0.0250.026-0.025-0.014-0.026-0.038-0.0240.0240.007-0.0040.0050.010-0.010-0.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
count-0.379-0.280-0.406-0.201-0.063-0.060-0.0551.0000.5900.2810.1620.3860.389-0.6290.553-0.2370.421-0.175-0.2560.177-0.178-0.3330.1370.0710.2420.2930.2660.3500.2560.0000.0770.0000.0470.5930.0280.0000.0000.0000.0000.1170.414
srv_count-0.3060.148-0.024-0.165-0.006-0.058-0.0530.5901.000-0.0090.0770.0430.0390.050-0.0550.2230.1200.4070.353-0.3660.028-0.027-0.095-0.026-0.119-0.0390.4790.2830.0950.0000.0930.0000.0120.2440.0000.0000.0000.0000.0000.0510.145
serror_rate-0.089-0.391-0.377-0.084-0.048-0.019-0.0180.281-0.0091.0000.8730.0020.024-0.3680.341-0.1640.208-0.303-0.3430.335-0.260-0.1970.8090.7400.0330.0590.1110.1620.3350.0510.0000.0000.0150.3020.0000.0000.0000.0000.0000.0570.299
srv_serror_rate-0.052-0.342-0.321-0.077-0.044-0.018-0.0170.1620.0770.8731.000-0.062-0.086-0.2380.225-0.0850.159-0.208-0.2490.243-0.214-0.1520.7230.812-0.039-0.0410.1110.1310.3360.0500.0000.0000.0140.2990.0000.0000.0000.0000.0000.0570.298
rerror_rate-0.025-0.500-0.528-0.089-0.042-0.027-0.0250.3860.0430.002-0.0621.0000.972-0.6310.609-0.0980.288-0.500-0.5610.475-0.352-0.246-0.021-0.1020.8260.8810.1810.2660.3300.0000.0190.0000.0270.3690.0210.0000.0000.0000.0000.0970.492
srv_rerror_rate-0.036-0.504-0.529-0.089-0.041-0.027-0.0250.3890.0390.024-0.0860.9721.000-0.6250.598-0.0940.282-0.499-0.5580.471-0.345-0.240-0.003-0.1190.8090.8990.1790.2160.3380.0000.0180.0000.0260.3690.0200.0000.0000.0000.0000.0970.483
same_srv_rate0.2110.6770.5880.1300.0820.0290.026-0.6290.050-0.368-0.238-0.631-0.6251.000-0.9630.219-0.3750.7140.770-0.6730.3330.325-0.288-0.185-0.552-0.5610.1240.3470.3000.0000.0230.0000.0430.5330.0250.0000.0000.0000.0000.0990.514
diff_srv_rate-0.178-0.657-0.560-0.126-0.082-0.029-0.0250.553-0.0550.3410.2250.6090.598-0.9631.000-0.1550.344-0.680-0.7380.663-0.290-0.2900.2760.1730.5280.5360.1550.2530.1260.0000.0000.0000.0120.2130.0000.0000.0000.0000.0000.0350.217
srv_diff_host_rate-0.0870.1670.278-0.0260.062-0.019-0.014-0.2370.223-0.164-0.085-0.098-0.0940.219-0.1551.000-0.2670.2580.262-0.2520.1010.313-0.137-0.117-0.177-0.1140.0960.2490.1160.0580.2000.0000.0280.3170.0000.0000.0000.0000.0000.0760.341
dst_host_count0.024-0.310-0.3350.0340.070-0.018-0.0260.4210.1200.2080.1590.2880.282-0.3750.344-0.2671.000-0.174-0.3790.315-0.676-0.8480.2190.1770.3950.3300.1470.1960.1260.0180.0830.0160.0330.3410.0300.0290.0060.0110.0370.0630.386
dst_host_srv_count-0.0740.6220.636-0.0420.060-0.048-0.038-0.1750.407-0.303-0.208-0.500-0.4990.714-0.6800.258-0.1741.0000.906-0.8490.1580.282-0.293-0.193-0.538-0.4800.1480.3780.2710.0150.0780.0140.0710.5880.0270.0000.0320.0180.0230.2490.597
dst_host_same_srv_rate-0.0680.6620.614-0.0260.058-0.034-0.024-0.2560.353-0.343-0.249-0.561-0.5580.770-0.7380.262-0.3790.9061.000-0.9180.3340.406-0.346-0.236-0.630-0.5540.1680.4030.2970.0000.0650.0420.1180.5680.0420.0170.0310.0330.0280.3170.583
dst_host_diff_srv_rate0.143-0.597-0.5740.051-0.0450.0210.0240.177-0.3660.3350.2430.4750.471-0.6730.663-0.2520.315-0.849-0.9181.000-0.269-0.3870.3530.2520.5960.4840.1250.2590.1580.0000.0270.0570.0230.2170.0550.0000.0000.0420.0500.0910.206
dst_host_same_src_port_rate-0.1300.3080.090-0.089-0.0920.0030.007-0.1780.028-0.260-0.214-0.352-0.3450.333-0.2900.101-0.6760.1580.334-0.2691.0000.556-0.269-0.240-0.449-0.4110.4830.2560.1180.0220.1410.0270.0280.2910.0000.0000.0000.0220.0420.0630.230
dst_host_srv_diff_host_rate-0.0810.2890.348-0.108-0.071-0.007-0.004-0.333-0.027-0.197-0.152-0.246-0.2400.325-0.2900.313-0.8480.2820.406-0.3870.5561.000-0.193-0.160-0.348-0.2820.3010.3200.0400.1100.3840.0150.0000.1310.0150.0000.0880.0440.0000.0220.102
dst_host_serror_rate0.065-0.387-0.320-0.007-0.025-0.0100.0050.137-0.0950.8090.723-0.021-0.003-0.2880.276-0.1370.219-0.293-0.3460.353-0.269-0.1931.0000.8300.0800.0800.1120.2160.3320.0420.0510.0090.0200.3170.0000.0000.0540.0000.0000.0600.321
dst_host_srv_serror_rate0.087-0.338-0.276-0.050-0.008-0.0050.0100.071-0.0260.7400.812-0.102-0.119-0.1850.173-0.1170.177-0.193-0.2360.252-0.240-0.1600.8301.000-0.015-0.0130.1140.2030.3520.0610.0000.0000.0170.3210.0140.0000.0470.0000.0000.0600.314
dst_host_rerror_rate0.159-0.504-0.4750.0480.059-0.014-0.0100.242-0.1190.033-0.0390.8260.809-0.5520.528-0.1770.395-0.538-0.6300.596-0.449-0.3480.080-0.0151.0000.8680.1760.2550.3420.0000.0350.0000.0420.4090.0110.0000.0000.0120.0000.0740.516
dst_host_srv_rerror_rate0.003-0.531-0.489-0.0210.088-0.013-0.0090.293-0.0390.059-0.0410.8810.899-0.5610.536-0.1140.330-0.480-0.5540.484-0.411-0.2820.080-0.0130.8681.0000.1820.2430.3680.0000.0190.0470.0400.4300.0390.0000.0000.0200.0000.0950.490
protocol_type0.0430.0000.0000.0000.0000.0000.0000.2660.4790.1110.1110.1810.1790.1240.1550.0960.1470.1480.1680.1250.4830.3010.1120.1140.1760.1821.0000.8610.2230.0000.1710.0000.0440.3920.0200.0000.0000.0120.0020.0750.227
service0.2120.2050.1340.0390.0000.0000.0000.3500.2830.1620.1310.2660.2160.3470.2530.2490.1960.3780.4030.2590.2560.3200.2160.2030.2550.2430.8611.0000.3210.2200.1770.0320.2160.9350.1260.0000.0290.0000.0870.9570.751
flag0.1430.0000.0940.0000.0000.0000.0000.2560.0950.3350.3360.3300.3380.3000.1260.1160.1260.2710.2970.1580.1180.0400.3320.3520.3420.3680.2230.3211.0000.0520.0270.0000.0430.5740.0290.0000.0000.0000.0000.1210.606
land0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0500.0000.0000.0000.0000.0580.0180.0150.0000.0000.0220.1100.0420.0610.0000.0000.0000.2200.0521.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.011
wrong_fragment0.0000.0000.0000.0000.0000.0000.0000.0770.0930.0000.0000.0190.0180.0230.0000.2000.0830.0780.0650.0270.1410.3840.0510.0000.0350.0190.1710.1770.0270.0001.0000.0000.0000.0590.0000.0000.0000.0000.0000.0060.038
urgent0.0000.0000.0000.0000.2580.4470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0140.0420.0570.0270.0150.0090.0000.0000.0470.0000.0320.0000.0000.0001.0000.0000.0210.3850.0000.2000.1650.0000.0000.014
num_failed_logins0.0000.0000.0000.1360.0000.0000.0000.0470.0120.0150.0140.0270.0260.0430.0120.0280.0330.0710.1180.0230.0280.0000.0200.0170.0420.0400.0440.2160.0430.0000.0000.0001.0000.1280.0000.0000.0000.0190.0000.1530.126
logged_in0.1310.0000.0160.0220.0070.0100.0000.5930.2440.3020.2990.3690.3690.5330.2130.3170.3410.5880.5680.2170.2910.1310.3170.3210.4090.4300.3920.9350.5740.0110.0590.0210.1281.0000.0540.0120.0310.0620.0220.1400.551
root_shell0.1190.0000.1410.0530.2690.3010.0000.0280.0000.0000.0000.0210.0200.0250.0000.0000.0300.0270.0420.0550.0000.0150.0000.0140.0110.0390.0200.1260.0290.0000.0000.3850.0000.0541.0000.2130.3740.2940.0600.0000.007
su_attempted0.4470.0000.3530.0000.7070.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.2131.0000.1280.5040.0000.0000.006
num_shells0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0320.0310.0000.0000.0880.0540.0470.0000.0000.0000.0290.0000.0000.0000.2000.0000.0310.3740.1281.0000.1110.0760.0000.022
num_access_files0.2250.0000.1810.2350.5800.5430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0180.0330.0420.0220.0440.0000.0000.0120.0200.0120.0000.0000.0000.0000.1650.0190.0620.2940.5040.1111.0000.0330.0000.034
is_host_login0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0230.0280.0500.0420.0000.0000.0000.0000.0000.0020.0870.0000.0000.0000.0000.0000.0220.0600.0000.0760.0331.0000.0000.016
is_guest_login0.0180.0000.0000.0990.0000.0000.0000.1170.0510.0570.0570.0970.0970.0990.0350.0760.0630.2490.3170.0910.0630.0220.0600.0600.0740.0950.0750.9570.1210.0000.0060.0000.1530.1400.0000.0000.0000.0000.0001.0000.125
class0.1260.0000.0180.0180.0060.0080.0000.4140.1450.2990.2980.4920.4830.5140.2170.3410.3860.5970.5830.2060.2300.1020.3210.3140.5160.4900.2270.7510.6060.0110.0380.0140.1260.5510.0070.0060.0220.0340.0160.1251.000

Missing values

2023-02-20T15:06:20.460433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-20T15:06:22.530459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
00tcpprivateREJ000000000000000000229100.00.001.01.00.040.060.00255100.040.060.000.000.000.001.001.00anomaly
10tcpprivateREJ00000000000000000013610.00.001.01.00.010.060.0025510.000.060.000.000.000.001.001.00anomaly
22tcpftp_dataSF1298300000000000000000110.00.000.00.01.000.000.00134860.610.040.610.020.000.000.000.00normal
30icmpeco_iSF20000000000000000001650.00.000.00.01.000.001.003571.000.001.000.280.000.000.000.00anomaly
41tcptelnetRSTO0150000000000000000180.00.121.00.51.000.000.7529860.310.170.030.020.000.000.830.71anomaly
50tcphttpSF267145150000010000000000440.00.000.00.01.000.000.001552551.000.000.010.030.010.000.000.00normal
60tcpsmtpSF10223870000010000000000130.00.000.00.01.000.001.00255280.110.720.000.000.000.000.720.04normal
70tcptelnetSF1291740000100000000000110.00.000.00.01.000.000.002552551.000.000.000.000.010.010.020.02anomaly
80tcphttpSF327467000001000000000033470.00.000.00.01.000.000.041512551.000.000.010.030.000.000.000.00normal
90tcpftpSF261570000100000000001110.00.000.00.01.000.000.0052260.500.080.020.000.000.000.000.00anomaly
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
225340tcpprivateREJ000000000000000000235100.00.01.01.00.040.060.00255100.040.070.000.000.000.01.001.00anomaly
225350tcphttpSF28060870000010000000000330.00.00.00.01.000.000.0052551.000.000.200.040.000.00.000.00normal
225360tcpiso_tsapREJ000000000000000000127180.00.01.01.00.140.060.00255180.070.050.000.000.000.01.001.00anomaly
225371tcpsmtpSF25992930000010000000000220.00.00.00.01.000.000.002551860.730.130.000.000.000.00.260.00anomaly
225380icmpecr_iSF10320000000000000000053530.00.00.00.01.000.000.002552551.000.001.000.000.000.00.000.00anomaly
225390tcpsmtpSF7943330000010000000000110.00.00.00.01.000.000.001001410.720.060.010.010.010.00.000.00normal
225400tcphttpSF31793800000100000000002110.00.00.00.01.000.000.181972551.000.000.010.010.010.00.000.00normal
225410tcphttpSF54540831400020110000000005100.00.00.00.01.000.000.202552551.000.000.000.000.000.00.070.07anomaly
225420udpdomain_uSF42420000000000000000460.00.00.00.01.000.000.332552520.990.010.000.000.000.00.000.00normal
225430tcpsunrpcREJ0000000000000000004100.00.01.01.00.251.001.00255210.080.030.000.000.000.00.441.00anomaly

Duplicate rows

Most frequently occurring

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass# duplicates
00tcpotherREJ00000000000000000048210.050.00.951.00.01.00.025510.01.00.00.00.070.00.931.0anomaly2
10tcpotherREJ00000000000000000048710.040.00.961.00.01.00.025510.01.00.00.00.020.00.981.0anomaly2
20tcpotherREJ00000000000000000050910.050.00.951.00.01.00.025510.01.00.00.00.030.00.971.0anomaly2